In nuclear measurements it is frequently helpful to determine the energy that a particle or photon has deposited in a detection device. These detection devices provide an electrical signal indicative of the amount of energy deposited in a single event. The energy distribution of these events, for example gamma rays from a multitude of elements, can be represented as a histogram, in which the abscissa represents the deposited energy or a function thereof, and the ordinate represents the number of events having a signal which falls into one of the discrete energy bins of the abscissa.
There are many variants of nuclear detectors. A nuclear detector typically includes the detecting material itself and devices to convert and/or amplify the signal. Such detectors can be semiconductor detectors such as Ge-detectors, scintillation detectors coupled to photon detectors, proportional counters, etc.
The purpose of a gamma ray spectroscopy system is to determine the energy associated with the absorption of incident gamma rays by the detector in what is referred to herein as pulse events. Pulse events can be registered in histograms organized by energy levels (Multi-Channel Analyzer, MCA spectra) and/or times of arrival (Multi-Channel Scaler, MCS spectra). The performance of such systems is measured in terms of energy resolution (i.e., ability to distinguish between separate but adjacent energy levels), time resolution (i.e., ability to distinguish between nearly coincident pulses), throughput (i.e., ability to process multiple adjacent pulses) and linearity (i.e., the linear relationship between deposited energy from a pulse and associated histogram channel).
Higher performance is achieved at higher count rates, or throughput, and higher energy resolution. Such systems can generally perform a specified measurement faster than slower systems and/or those with poorer resolution. This is due to both improved statistical uncertainties from the larger number of events counted and measured, and the ability to better separate energy peaks and features in the measured spectrum.
Referring to FIG. 1, a prior art gamma ray spectroscopy system 100 includes a scintillator detector 102 coupled to a photon-electron converter 104, a bias supply 106, pre-amplifier 108, shaping amplifiers 110, a pulse height/MCS analyzer 112, histogram acquisition memory 114, controller 116, user interface 118, network interface 120, and recording memory 122. These spectroscopy systems have applications in many industries and sciences. Some applications involve a stationary measurement in a lab, where a stable measurement can be performed under controlled conditions.
One particular application of interest is well logging. The detector moves through a subterranean borehole using various modes of conveyance to traverse rock formations of varying minerals, fluids, and structure. High performance systems can traverse the formations faster, achieving better statistical uncertainties and measurement quality than less performant systems. This is desirable for reducing the overall cost of measurement, especially if the measurement is performed in conditions where time in the borehole is costly, such as deepwater drilling rigs or high volume drilling operations. Related applications include fluid flow (pipeline) or material flow (conveyor) where the spectroscopy system may be stationary, but the material in the volume of investigation is continually changing due to material motion.
Methods to keep the histogram calibrated such that each bin is aligned with a specified energy range are known in the art as techniques for gain regulation. These methods usually adjust the gain of the acquisition system such that electrical signal amplitudes corresponding to each bin are properly aligned with gamma ray energies.
Accurate and stable gain regulation is critical for high performance spectroscopy systems, which typically have high count rate and high resolution specifications. However, in some applications such as well logging, the count rate is not only very high, but also highly variable. The energy distribution of the count rates may also vary as the measured volume around the detector changes. For a given bias supply setting, the energy calibration of the detector and associated devices may vary with count rate or energy distribution. This can be due to “loading” of the detection devices as the cumulative amount of charge changes, which may alter device characteristics such as gain. Or it could be due to signal processing effects in the system, which may vary with count rate or energy distribution.
If the system gain varies with count rate, then the acquired spectrum will be distorted. Often this will appear as if the energy resolution of the system is degraded. Small gain variations may impair statistical uncertainty during analysis of the spectrum, as peaks will be broadened. Larger gain variations may render the spectrum unusable, by creating multiple peaks or other distortions beyond the capability of typical spectroscopy processing algorithms. For high performance systems, it is critically important to prevent count rate induced variations from distorting the spectrum.
Gain regulation may operate by acquiring a spectrum for a long enough time to detect a spectral feature such as an energy peak or edge. Energy bins in the spectrum may have acquired enough counts that statistical uncertainty is small enough to compare bins and evaluate features. Techniques employed in the art include moments, peak detection, peak fitting, fitting of standards (unique spectral shapes for each element encountered), and so on. Some such methods may use considerable processing to be performed on the measured spectrum, which translates into additional delay before a correction can be applied to the system.
In most cases, the time spent to acquire a spectrum for gain analysis is longer than the time to perform individual measurements, and much longer than the time the logging equipment spends in the vicinity of a sample volume. For example, the logging device may move axially through the borehole at 1 foot per second. A typical volume of investigation spans approximately one foot or so axially. Spectra are acquired at perhaps 0.5 second intervals. Thus spectra may be changing significantly every 1 to 2 seconds as new volumes of rock are sampled.
However, some gain analysis techniques may use 5 to 10 seconds of data to achieve usable statistical precision. In some existing systems 60 seconds or more are used. Most closed loop control algorithms use several iterations to correct an error, so the response time of typical gain regulation algorithms is far too slow (20 seconds or more) to compensate for gain variations due to the rate (1 to 2 seconds) at which materials are measured. By the time a typical closed loop gain regulation algorithm has detected and compensated for a gain change, the condition stimulating the change may be long past.
The severity of the distortion to the measured spectra may depend on logging speed (rate of traversal), variations in successive formations, gain sensitivity of the spectroscopy system to count rate variation, energy resolution, and the speed of the control loops. High performance systems typically move faster through the formations, typically use very high count rates which may increase gain sensitivity to count rate variation, and have very fine energy resolution—which reduces the tolerance to gain variations.
Some regulation algorithms have been designed to accurately adjust the gain of the acquisition system over the long term, compensating for slow drift such as temperature changes. Such algorithms are, however, usually inadequate for short term gain changes, especially since a gain change may first be detected before it can be corrected. A method is needed to adjust the system gain in the short term (between the slower control updates from the traditional techniques), and make the appropriate changes to compensate for stimuli that affect the system gain without disrupting the slow, but accurate control provided by the traditional techniques.